Qinglong Lu (卢庆龙)

Qinglong Lu (卢庆龙)

Learning, thinking, and practicing.

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Pages

Posts

Future Blog Post

less than 1 minute read

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Blog Post number 4

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Blog Post number 3

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Blog Post number 2

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Blog Post number 1

less than 1 minute read

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conferences

Gao, J., Lu, Q. L. & Cai, M. (2020). Quantifying privacy vulnerability under linkage attack across multi-source individual mobility data. In 99th Transportation Research Board (TRB) Annual Meeting.

Published:

With the advances in detector and sensor technologies, identity detection-based intelligent transportation systems—such as license plate recognition (LPR) system and parking electronic toll collection (ETC) system—have been widely deployed in urban transportation, generating large quantities of multi-source individual-based mobility data set (e.g., LPR data and parking data). Given the high frequency, precision and wide coverage, these individual-based mobility data can be used in many transportation research areas, such as transportation planning, traffic prediction and individual mobility pattern profiling. With the increasing demand for publishing and sharing these individual-based data sets to researchers and practitioners, the privacy issue of data publishing has been a major concern since true identities of individuals can be revealed by linkage attack. In this paper, we quantitatively measure the privacy disclosure risk caused by linkage attack across multi-source individual-based mobility data sets. Taking an example of LPR data and parking data, a traffic-knowledge-driven adversary model is proposed for linkage attack conducting among LPR data and parking data. Two common modes of LPR data publishing are examined and two quantitative criteria are introduced to present the risk of privacy leakage under linkage attack. The experimental results demonstrate that anonymized individual still under high risk of being linked successfully (71.63% under mode 1 and 36.55% under mode 2). This study serves as a wake-up call for relevant agencies and data owners about the privacy vulnerability caused by linkage attack across multi-source individual-based mobility data.

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Qurashi, M., Lu, Q., Cantelmo, G., & Antoniou, C. (2020). PC-SPSA: Implementation assessment and exploration of different historical data-set generation methods for enhanced DTA model calibration. In 3rd Symposium on Management of Future Motorway and Urban Traffic Systems (MFTS).

Published:

Calibrating DTA models is complex due to the involved undeterminedness, non-linearity, and dimensionality, restricting calibration approaches especially when calibrating larger networks. Simultaneous perturbation stochastic approximation (SPSA) has been proposed for the DTA model calibration, with encouraging results, for more than a decade with multiple variants trying to improve its application scalability on larger networks. Recently, PC-SPSA has been proposed, combining Principal Component Analysis (PCA) with SPSA to reduce the problem dimensions and non-linearity by limiting the search space in lower dimension space based on orthogonal Principal Components evaluated upon a set of historical estimates. In this paper, we further explore PC-SPSA implementation by assessing its sensitivity towards SPSA parameters definition, its performance in calibrating synthetic problems of different dimensions and non-linearity, and formulating multiple OD historical data–set generation methods for improved calibration (in case of non-existent or irrelevant historical estimates). The performance of each method is compared calibrating an urban network of Munich with similar PC-SPSA settings, depicting more correlated generation techniques perform better consistently than simplified ones.

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Qurashi, M., Lu, Q., Cantelmo, G., & Antoniou, C. (2020) PC-SPSA: Exploration and assessment of different historical data–set generation methods for enhanced DTA model calibration. In 9th Symposium of the European Association for Research in Transportation (hEART2020).

Published:

This paper explores multiple historical data-set estimation methods which are crucial for the calibration performance for principal component analysis (PCA) based algorithms. We first propose multiple sets of historical data-set generation methods with probable calibration scenarios (which replicate more realistic changes within the structure of the demand) and later explore the performance of all the proposed historical data-sets with PC-SPSA to understand the importance of different historical data-set generation parameters. As per the current results, more correlatedly generated historical estimates (i.e. method 3 and 6) outperform other simplified techniques but it will be further interesting to explore and analyze their performance calibrating other different sets of scenarios. Next steps, to be shown in hEART2020 conference, will include, the exploration of all the proposed methods on the possible demand scenarios to identify the best most generically wellperforming data-set generation technique, and later validating that technique on a larger network of Munich city (with a network of 8689 links, 706 detector location and demand of OD matrix [73 × 73] or 5329 OD pairs) with different demand scenarios and also other network information e.g. travel times etc. Finally, results proposed in this study are still based on synthetic experiments. This is a limitation, as we aim to test PCA based algorithms when historical data sets are not available (or information is not reliable). To do so, we will use real traffic data from Munich to generate a benchmark scenario that is assumption free - e.g. the “true” network state is derived from real data and not from syntetic functions. This will allow us to validate our probability function against real data in an assumption free scenario.

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Lu, Q.L., Yang, K., & Antoniou, C. (2021). Crash risk analysis for the mixed traffic flow with human-driven and connected and autonomous vehicles. In 24th IEEE International Conference on Intelligent Transportation Systems (ITSC).

Published:

In the near future, traditional or low-automation vehicles will share the roads with Connected and Autonomous Vehicles (CAVs) over many years. Yet, this complexity may impose new unknowns on the real-time crash risk evaluation. Consequently, it is important to explore crash risk analysis in such kind of mixed traffic flow environments. This paper constructed several special traffic variables in mixed traffic flow environments and proposed the kernel logistic regression (KLR) model to evaluate the crash risk in real-time. A simulated urban expressway corridor based on the North-South Elevated Road in Shanghai, China, is developed in SUMO, for the purpose of collecting the traffic safety data and traffic data (i.e., virtual detector data and Global Navigation Satellite System (GNSS) data) in mixed traffic flow environments. The prediction performance of KLR models was tested and analyzed with the simulated data, and is also compared with that of support vector machines (SVM) models. The results show that KLR has a good prediction performance like SVM. Considering KLR can provide probability estimates directly and can naturally extend to multi-class classification, priority should be given to KLR in similar problems, especially when crash risk is classified into multiple levels. The proposed KLR model is therefore recommended and has the potential to evaluate the real-time crash risk in the mixed traffic flow environment.

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Lu, Q.L., Qurashi, M., & Antoniou, C. (2022). A Stochastic Programming Method for OD Estimation Using LBSN Check-In Data. In 4th Symposium on Management of Future Motorway and Urban Traffic Systems.

Published:

Dynamic OD estimators based on traffic measurements inevitably encounter the indeterminateness problem on the posterior OD flows as such systems structurally have more unknowns than constraints. To resolve this problem and take advantage of the emerging urban mobility data, the paper proposes a dynamic OD estimator based on location-based social networking (LBSN) data, leveraging the two-stage stochastic programming framework, under the assumption that similar check-in patterns are generated by the same OD pattern. The search space of the OD flows will be limited by integrating a batch of realizations/scenarios of the second-stage problem state (i.e. check-in pattern) in the model. The two-stage stochastic programming model decomposes in a master problem and a set of subproblems (one per scenario) via the Benders decomposition algorithm, which will be tackled alternately. The preliminary results from experiments conducted with the Foursquare data of Tokyo, Japan, show that the proposed OD estimator can effectively recurrent the check-in patterns and result in a good posterior OD estimate.

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Lu, Q.L., Qurashi, M., & Antoniou, C. (2023). A Two-Stage Stochastic Programming approach for Dynamic OD Estimation. In 102th TRB Annual Meeting 2023.

Published:

Estimating origin-destination (OD) demand is indispensable for urban transport management and traffic control systems. While the existing estimation methods rely on data sources like household travel surveys and traffic network detection, they incur very high costs and are still either less frequent or low in coverage density triggering lower observability and indeterminacy issues for OD estimation. With ubiquity of smartphones, Location based social networks (LSBN) data has emerged as a new rich data source with broad urban spatial and temporal coverage highly suitable for OD estimation. However, thus far, most LSBN-based estimation models only focus on static (day-level) OD estimation. This paper establishes a two-stage stochastic programming (TSSP) framework integrating the activity chains to model activity-level mobility flows using LBSN data. The first stage model aims to minimize the errors introduced by the inter-zone OD flows alongside the expected errors of the check-in patterns. The second stage model attempts to minimize the errors produced by the considered check-in pattern scenarios. A generalized Benders decomposition algorithm is presented to solve the two-stage stochastic programming model. We conduct the experiments employing generalized least squares (GLS) estimator on the case study of Tokyo city. The results depict that the algorithm convergence can be guaranteed within several steps. The algorithm shows satisfactory performance in check-in pattern estimation, OD flows estimation, and activity share estimation. Further, the implementation of the model in practical applications is also specifically discussed.

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news

publications

Liu, Z. J., Lu, Q. L., & Gao, J. (2024). A similarity-based data-driven car-following model considering driver heterogeneity. Transportation Research Procedia, 78, 611-618.

Published in Transportation research procedia, 2024

Human drivers usually have distinct driving patterns and preferences. Driver heterogeneity is crucial for modeling driving behaviors. This paper incorporates driver heterogeneity with data-driven approaches to predict car-following behaviors. A bi-level similarity-based car-following model is proposed to predict the vehicle’s moving distance. In the upper level, drivers with similar driving patterns as the ego vehicle are identified using k-nearest neighboring (kNN) search. In the lower level, leveraging kNN model, candidate records are selected from the identified vehicles’ trajectories and applied to predict the ego vehicle’s moving distance, combining the driving pattern similarity measured in the upper level. By taking into account the driver heterogeneity, the proposed model is capable of identifying the most relevant driving situations, which leads to an improvement of prediction accuracy. Furthermore, the established bi-level structure largely shrinks the searching space of candidate records, which reduces the searching complexity and enhances computational efficiency. We quantitatively evaluate and compare the performance of the proposed model in terms of both prediction accuracy and computational efficiency using real-world vehicle trajectory data.

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teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

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